


import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
 import jax
 import jax.numpy as jnp
 from flax.jax_utils import replicate
 from flax.training.common_utils import shard






@slow
@require_flax
class 	__lowercase					(	unittest.TestCase	):





      def 							_lowercase					(		self    :       Tuple							)     -> Dict:






         """simple docstring"""







         super().tearDown()
         gc.collect()





      def 							_lowercase					(		self    :       Dict							)     -> List[str]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  FlaxControlNetModel.from_pretrained(
             """lllyasviel/sd-controlnet-canny"""       ,							from_pt=_UpperCAmelCase       ,							dtype=jnp.bfloataa							)
         UpperCAmelCase					,	UpperCAmelCase      					=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
             """runwayml/stable-diffusion-v1-5"""       ,							controlnet=_UpperCAmelCase       ,							from_pt=_UpperCAmelCase       ,							dtype=jnp.bfloataa							)
         UpperCAmelCase      					=  controlnet_params

         UpperCAmelCase      					=  """bird"""
         UpperCAmelCase      					=  jax.device_count()
         UpperCAmelCase      					=  pipe.prepare_text_inputs([prompts] * num_samples							)

         UpperCAmelCase      					=  load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""							)
         UpperCAmelCase      					=  pipe.prepare_image_inputs([canny_image] * num_samples							)

         UpperCAmelCase      					=  jax.random.PRNGKey(0							)
         UpperCAmelCase      					=  jax.random.split(_UpperCAmelCase       ,							jax.device_count()							)

         UpperCAmelCase      					=  replicate(_UpperCAmelCase							)
         UpperCAmelCase      					=  shard(_UpperCAmelCase							)
         UpperCAmelCase      					=  shard(_UpperCAmelCase							)

         UpperCAmelCase      					=  pipe(
             prompt_ids=_UpperCAmelCase       ,							image=_UpperCAmelCase       ,							params=_UpperCAmelCase       ,							prng_seed=_UpperCAmelCase       ,							num_inference_steps=5_0       ,							jit=_UpperCAmelCase       ,							).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         UpperCAmelCase      					=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
         UpperCAmelCase      					=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         UpperCAmelCase      					=  jnp.asarray(jax.device_get(image_slice.flatten()							)							)
         UpperCAmelCase      					=  jnp.array(
             [0.167_969, 0.116_699, 0.081_543, 0.154_297, 0.132_812, 0.108_887, 0.169_922, 0.169_922, 0.205_078]							)
         print(F"""output_slice: {output_slice}"""							)
         assert jnp.abs(output_slice - expected_slice							).max() < 1e-2





      def 							_lowercase					(		self    :       Tuple							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase					,	UpperCAmelCase      					=  FlaxControlNetModel.from_pretrained(
             """lllyasviel/sd-controlnet-openpose"""       ,							from_pt=_UpperCAmelCase       ,							dtype=jnp.bfloataa							)
         UpperCAmelCase					,	UpperCAmelCase      					=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
             """runwayml/stable-diffusion-v1-5"""       ,							controlnet=_UpperCAmelCase       ,							from_pt=_UpperCAmelCase       ,							dtype=jnp.bfloataa							)
         UpperCAmelCase      					=  controlnet_params

         UpperCAmelCase      					=  """Chef in the kitchen"""
         UpperCAmelCase      					=  jax.device_count()
         UpperCAmelCase      					=  pipe.prepare_text_inputs([prompts] * num_samples							)

         UpperCAmelCase      					=  load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""							)
         UpperCAmelCase      					=  pipe.prepare_image_inputs([pose_image] * num_samples							)

         UpperCAmelCase      					=  jax.random.PRNGKey(0							)
         UpperCAmelCase      					=  jax.random.split(_UpperCAmelCase       ,							jax.device_count()							)

         UpperCAmelCase      					=  replicate(_UpperCAmelCase							)
         UpperCAmelCase      					=  shard(_UpperCAmelCase							)
         UpperCAmelCase      					=  shard(_UpperCAmelCase							)

         UpperCAmelCase      					=  pipe(
             prompt_ids=_UpperCAmelCase       ,							image=_UpperCAmelCase       ,							params=_UpperCAmelCase       ,							prng_seed=_UpperCAmelCase       ,							num_inference_steps=5_0       ,							jit=_UpperCAmelCase       ,							).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         UpperCAmelCase      					=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]							)
         UpperCAmelCase      					=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         UpperCAmelCase      					=  jnp.asarray(jax.device_get(image_slice.flatten()							)							)
         UpperCAmelCase      					=  jnp.array(
             [[0.271_484, 0.261_719, 0.275_391, 0.277_344, 0.279_297, 0.291_016, 0.294_922, 0.302_734, 0.302_734]]							)
         print(F"""output_slice: {output_slice}"""							)
         assert jnp.abs(output_slice - expected_slice							).max() < 1e-2








'''simple docstring'''

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}

class 							a__( snake_case__     ):
			a_	: int									=      '''efficientnet'''






			def __init__(     self					,   _UpperCAmelCase = 3					,   _UpperCAmelCase = 600					,   _UpperCAmelCase = 2.0					,   _UpperCAmelCase = 3.1					,   _UpperCAmelCase = 8					,   _UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]					,   _UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]					,   _UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]					,   _UpperCAmelCase = []					,   _UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]					,   _UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]					,   _UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]					,   _UpperCAmelCase = 0.25					,   _UpperCAmelCase = "swish"					,   _UpperCAmelCase = 2560					,   _UpperCAmelCase = "mean"					,   _UpperCAmelCase = 0.02					,   _UpperCAmelCase = 0.001					,   _UpperCAmelCase = 0.99					,   _UpperCAmelCase = 0.5					,   _UpperCAmelCase = 0.2					,   **_UpperCAmelCase					,   )							->							Dict:
					super().__init__(**_UpperCAmelCase						)

					snake_case__	     =num_channels
					snake_case__	     =image_size
					snake_case__	     =width_coefficient
					snake_case__	     =depth_coefficient
					snake_case__	     =depth_divisor
					snake_case__	     =kernel_sizes
					snake_case__	     =in_channels
					snake_case__	     =out_channels
					snake_case__	     =depthwise_padding
					snake_case__	     =strides
					snake_case__	     =num_block_repeats
					snake_case__	     =expand_ratios
					snake_case__	     =squeeze_expansion_ratio
					snake_case__	     =hidden_act
					snake_case__	     =hidden_dim
					snake_case__	     =pooling_type
					snake_case__	     =initializer_range
					snake_case__	     =batch_norm_eps
					snake_case__	     =batch_norm_momentum
					snake_case__	     =dropout_rate
					snake_case__	     =drop_connect_rate
					snake_case__	     =sum(_UpperCAmelCase						) * 4





class 							a__( snake_case__     ):
			a_	: List[str]									=      version.parse('''1.11'''     )






			@property
			def 						_lowercase  (     self						)							->							Mapping[str, Mapping[int, str]]:
					return OrderedDict(
					    [
					        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
					    ]						)






			@property
			def 						_lowercase  (     self						)							->							float:
					return 1E-5




